1 / 33

Portable BCI Stimulator

Portable BCI Stimulator. Final Presentation Group: 17 Bonnie Chen, Siyuan Wu, Randy Lefkowitz TA: Ryan May ECE 445 Monday, April 29 th , 2013. Overview. Introduction Features BCI/EEG System Overview Design of Individual Modules Testing and Verifications Future Development Sponsors.

mercia
Download Presentation

Portable BCI Stimulator

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Portable BCI Stimulator Final Presentation Group: 17 Bonnie Chen, Siyuan Wu, Randy Lefkowitz TA: Ryan May ECE 445 Monday, April 29th, 2013

  2. Overview • Introduction • Features • BCI/EEG System Overview • Design of Individual Modules • Testing and Verifications • Future Development • Sponsors

  3. Introduction • Most brain computer interfaces (BCI) limited to laboratory settings • We would like to help make the EEG system more portable through bluetooth • Allows people to communicate without any type of movement

  4. Features • Portability • Wearable Size • Rechargeable Battery • Wireless control • Bluetooth • Compatible with most computers • Variable frequency and Intensity • Set by User

  5. BCI/EEG System Overview

  6. Stimulator Top Level

  7. Computer/Wireless Module Overview • Wireless communication between PC and Bluetooth Module through terminal • Retrieve LED number, frequency and intensity level from user • Check the validity of command

  8. Wireless Transmitter/Receiver • Built in Bluetooth 2.0 communication • Standard TTL Bluetooth receiver • Data sent wirelessly from PC to Arduino

  9. Receiver – Arduino Diagram

  10. Input Flowchart

  11. Microcontroller Module • Calculates Runtime • Determines if each LED should toggle • Sets LED values • Latches values into LED driver

  12. Timing Flowchart

  13. Dividing Interval for On and Off • Calculate LED state time (on/off) • Interval = current – previous • Compare to Required Time • 1/(2*frequency) • Toggle if needed • Save new time • Previous = current

  14. TL5940 LED Driver Overview • 16 Output Channels • Rref = 2k ohms • Intensity set by PWM • Frequency controlled by Arduino TLC5940 library

  15. TLC-5940 Library • TLC.set(channel,intensity); • Loads TLC Register • Tlc.update(); • Latches data into LED driver

  16. TLC-5940 Arduino Connections

  17. 7.4V Power Supply Venom 1250mAh 10C 7.4V Lithium Ion Battery

  18. LED Array • Powered by 5V output from Arduino • Flashes at frequency values between 1-9 Hz based on Arduino Code • LED Intensities based on PWM values from LED Driver • 5-10 LEDs mounted on adjustable frame

  19. Lilypad Micro LEDs • 3.3mm long • Forward Voltage of 3.2-4.0V • 200mA forward current • Power Dissipation of 120mW

  20. PCB Schematic

  21. PCB Design Top Bottom

  22. Final Design

  23. Demo with the EEG

  24. Review of Requirements • Wireless Communication • Portability • Sufficient Power • Successful Classification over different frequencies on EEG System

  25. Testing and Verifications • EEG Classification • Frequency Bandwidth • Power Budget

  26. EEG Classification • Demo Frequencies • 6, 7, 8, 9 Hz • Classification • All 4 Frequencies classified correctly (within 0.3Hz) • Intensity of 20 out of 4096 • Fast Response • Comfortable Viewing

  27. Frequency Bandwidth • Record LED Driver Output on Oscilloscope • Analyze EEG data with MATLAB • Compare Variance with EEG Classifier Sensitivity • Adjust Sensitivity values of EEG Program Accordingly • Test user response on EEG with updated sensitivity values

  28. Frequency Bandwidth Cont. 1 Hz: μ = 1.0912, σ2 = 0.41 6 Hz: μ = 6.0694, σ2 = 0.36 7 Hz: μ = 7.1238, σ2 = 1.37 8 Hz: μ = 8.1937, σ2 = 2.01 9 Hz: μ = 9.2745, σ2 = 5.22 10 Hz: μ = 10.495, σ2 = 10.77 • Higher Frequencies produced less stable results • SSVEP measurements generally 5-15 Hz • Less accurate frequencies cause slower EEG response times

  29. Frequency Bandwidth Cont.

  30. Power Budget Component Imax (mA) Voltage Microcontroller 40 * 2 output pins 7-12V (ideal) LEDs 20 * (10 LEDs) 3.2V (green, white) Bluetooth Module 40 3.3V LED Driver 120 5V ________________ _________________ _________________ Total 440 ----------------- Estimated Usage time = 1250 [mAh] / 440 [mA] ≅ 3 hours of charge Factors to consider: - PWM value will never be over 50% (the blinking LED is on less than half of the time) - Able to get same results using 5 LEDs instead of 10 LEDs

  31. Future Development • Safer operating limits for near-eye LEDs • Determine ideal threshold for response time, classification, and stability of the system. • Improvements on mounting frame mechanics (aesthetics and functionality) • Use Feedback from the EEG to implement commands that can control a range of devices (Quadcopter, Paralysis Assistance)

  32. Sponsors A special thanks to the following people who helped make this project happen • James Norton (Beckman) • Erik Johnson (Beckman) • David Jun (Beckman) • Ryan May • Professor Carney • The friendly folks in the ECE parts shop

  33. Thanks!!

More Related